Engineering Program Manager

APPLE INC has the following available in Cupertino, California and various unanticipated locations throughout the USA. Collaborate with R&D partners to understand and define their data requirements from inception to delivery. Design and implement ML Data Ops strategies optimized for each feature (collection and annotation), including the identification and sourcing or creation of necessary tooling, equipment or crowd. Drive enhancements of data operations (increase scalability, diversity and quality, reduce cost and lead time), through innovative workflows that combine human and machine computation (leveraging capabilities of ML and foundation models). Work closely with privacy, legal, procurement, and product security teams to identify and clear options considered for data operations. Thoroughly scope projects, estimating timelines, cost, and identifying potential challenges in advance. Coordinate data programs across internal data functions (data engineering, QA) and other partners. Establish clear guidelines and training material. Collaborate with vendors to ensure tasks are calibrated appropriately; track and report on quantity and quality metrics. 40 hours/week. At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $141,800 - $213,700/yr and your base pay will depend on your skills, qualifications, experience, and location.

PAY & BENEFITS: Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits: https://www.apple.com/careers/us/benefits.html.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Bachelor’s degree or foreign equivalent in Computer Science, Computer Engineering or related field and 3 years of experience in the job offered or related occupation.

Experience and/or education must include:

Managing complex cross-team projects and delivering successful results across AIML Data Ops, R&D, QA, and vendor teams to drive on-time delivery of high-quality datasets.
Using JIRA and Confluence to track project timelines, including experience collaborating and assigning tasks to internal data PMs, vendor partners, and R&D stakeholders to monitor throughput, quality, and dependencies.
Using Python, SQL, Confluence, and Tableau, with experience in automation and creating custom reporting to track vendor productivity, visualize data quality metrics, and inform cost and resource planning decisions.
Utilizing Keynote, Numbers, and Pages to provide succinct, accurate executive summaries of complex technical and operational situations for senior leadership.
Identifying risks, developing mitigation strategies, and facilitating resolutions for project roadblocks in large-scale ML data collection and annotation programs involving multiple vendors and evolving model requirements.
Collaborating with R&D partners to define data requirements and translate model-training objectives into clear collection and annotation specifications, ensuring datasets meet accuracy, diversity, and coverage goals.
Leveraging automation and ML-assisted workflows (e.g., LLM-in-the-loop labeling, foundation-model validation) to improve scalability, reduce cost, and accelerate data-collection cycles.

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